SETL: A programmable semantic extract-transform-load framework for semantic data warehouses

Nath, Rudra Pratap Deb; Hose, Katja; Pedersen, Torben Bach; Romero, Óscar

doi:10.1016/j.is.2017.01.005

Cited by 37 publications

(21 citation statements)

References 23 publications

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“…Recent studies have extended the benefits of the semantic model to include data extract, transform and load processes which is highlighted in the case study. According to Rudra Pratap et al [20], in order to create better decisions for business analytics, organisations increasingly use structured, semi-structured, and unstructured data in addition to the (mostly structured) internal data. The current Extract-Transform-Load (ETL) tools are not suitable for this 'open world scenario' because they do not consider semantic issues in the integration processing.…”

Section: Discussionmentioning

confidence: 99%

A Semantic Data Model: Meaning Making from Data Structures in the SQL Server

Ramesh

Henderson

2018

JISEBI

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Information systems designs are increasingly concerned with entity relationships and technical programmatic approaches to solutions architecture as opposed to semantic based, business focused information architecture that places business definitions at the centre of the information system design and implementation. The disconnect between information technology and business is perpetuated by an overly prescriptive information technology technical design method that fails to incorporate qualitative and normative aspects of business, where information is structured and delivered according to business. The paper will discuss various decision support and semantic approaches to information design and delivery and argue that the traditional modes of solution delivery do not include meaning making of the data elements which are essential to business information reporting and analytics. The meaning making aspect identified is linked to data dictionary or business data glossary that allows for the discovery of semantic meaning from the SQL Server. Using Christian Fürber’s methodology on semantic programming, the analytics team developed a semantic model that enabled detailed definition of fields and the discovery of information using semantic search functionality embedded in the SQL Server. The project provided semantic data framework that provided business with the capability for semantic reconciliation and data sets that were further integrated with Tableau visualization and SQL auto processes.

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Section: Discussionmentioning

confidence: 99%

A Semantic Data Model: Meaning Making from Data Structures in the SQL Server

Ramesh

Henderson

2018

JISEBI

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“…Therefore, RDF data cubes from statistical and environmental domains [10,12,43] are published with an extended QB vocabulary. Moreover, semantic Extract-Transform-Load (ETL) tools automate and ease the process of annotating and publishing open data with QB4OLAP on the Semantic Web [5,31,32]. Therefore, we can see more and more multi-dimensional datasets annotated with QB4OLAP on the Semantic Web.…”

Section: Data Modeling and Representationmentioning

confidence: 99%

Multidimensional enrichment of spatial RDF data for SOLAP

Gür

Pedersen

Hose

et al. 2021

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Large volumes of spatial data and multidimensional data are being published on the Semantic Web, which has led to new opportunities for advanced analysis, such as Spatial Online Analytical Processing (SOLAP). The RDF Data Cube (QB) and QB4OLAP vocabularies have been widely used for annotating and publishing statistical and multidimensional RDF data. Although such statistical data sets might have spatial information, such as coordinates, the lack of spatial semantics and spatial multidimensional concepts in QB4OLAP and QB prevents users from employing SOLAP queries over spatial data using SPARQL. The QB4SOLAP vocabulary, on the other hand, fully supports annotating spatial and multidimensional data on the Semantic Web and enables users to query endpoints with SOLAP operators in SPARQL. To bridge the gap between QB/QB4OLAP and QB4SOLAP, we propose an RDF2SOLAP enrichment model that automatically annotates spatial multidimensional concepts with QB4SOLAP and in doing so enables SOLAP on existing QB and QB4OLAP data on the Semantic Web. Furthermore, we present and evaluate a wide range of enrichment algorithms and apply them on a non-trivial real-world use case involving governmental open data with complex geometry types.

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“…The strength of a data warehouse lies in a large amount of data and statistical analysis [2]. Huge data sets are extracted, formatted, and stored in a data center to produce output in the form of analysis results [3]. So far, the data warehouse has been applied in the business of sales [4], finance and banking [5], agriculture [6], and also in the medical world [7].…”

Section: Introductionmentioning

confidence: 99%

Implementation of editorial television data warehouse: Health and business news will keep high since pandemic Covid-19

Suni¹,

Devianto²

2021

World J. Adv. Eng. Tech. Sci.

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Data warehouse technology in the world of informatics can be applied in television editors to predict events in the future. The results of implementing a data warehouse in television editors since the Covid-19 pandemic shows that in the future, television coverage will still be dominated by health news 80.64 percent and economic news 65.61 percent. In Indonesia, legal and criminal news reports have also become a news trend as a result of these health and economic problems. The television editorial data warehouse was designed using Kimball's Nine-Step method and implementation using Microsoft SQL Server and Tableau. And to predict the news in the future analyzed using the Markov model. Apart from analyzing television news, this data warehouse is also capable know the performance of the television editorial team, namely the performance of news producers, reporters, and cameramen. The results of this data warehouse can be used by television management and chief editors to make informed strategic decisions.

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SETL: A programmable semantic extract-transform-load framework for semantic data warehouses

Cited by 37 publications

References 23 publications

A Semantic Data Model: Meaning Making from Data Structures in the SQL Server

A Semantic Data Model: Meaning Making from Data Structures in the SQL Server

Multidimensional enrichment of spatial RDF data for SOLAP

Implementation of editorial television data warehouse: Health and business news will keep high since pandemic Covid-19

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